Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …

Climate modelling and analysis are facing new demands to enhance projections and
climate information. Here we argue that now is the time to push the frontiers of machine …

Class-incremental learning (CIL) aims to adapt to emerging new classes without forgetting
old ones. Traditional CIL models are trained from scratch to continually acquire knowledge …

Over the last decade, neural networks have reached almost every field of science and
become a crucial part of various real world applications. Due to the increasing spread …

Neural networks (NNs) are currently changing the computational paradigm on how to
combine data with mathematical laws in physics and engineering in a profound way …

The posterior over Bayesian neural network (BNN) parameters is extremely high-
dimensional and non-convex. For computational reasons, researchers approximate this …

Machine learning (ML) systems often exhibit unexpectedly poor behavior when they are
deployed in real-world domains. We identify underspecification in ML pipelines as a key …

The key distinguishing property of a Bayesian approach is marginalization, rather than using
a single setting of weights. Bayesian marginalization can particularly improve the accuracy …

Large datasets have become commonplace in NLP research. However, the increased
emphasis on data quantity has made it challenging to assess the quality of data. We …

In this paper, we conjecture that if the permutation invariance of neural networks is taken into
account, SGD solutions will likely have no barrier in the linear interpolation between them …